{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation\n",
    "from keras.activations import relu, sigmoid\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Description of the data:\n",
    "\n",
    "- instant: record index\n",
    "- dteday : date\n",
    "- season : season (1:springer, 2:summer, 3:fall, 4:winter)\n",
    "- yr : year (0: 2011, 1:2012)\n",
    "- mnth : month ( 1 to 12)\n",
    "- hr : hour (0 to 23)\n",
    "- holiday : weather day is holiday or not (extracted from [Web Link])\n",
    "- weekday : day of the week\n",
    "- workingday : if day is neither weekend nor holiday is 1, otherwise is 0.\n",
    "+ weathersit : \n",
    "  - 1: Clear, Few clouds, Partly cloudy, Partly cloudy\n",
    "  - 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\n",
    "  - 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds\n",
    "  - 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog\n",
    "- temp : Normalized temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-8, t_max=+39 (only in hourly scale)\n",
    "- atemp: Normalized feeling temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-16, t_max=+50 (only in hourly scale)\n",
    "- hum: Normalized humidity. The values are divided to 100 (max)\n",
    "- windspeed: Normalized wind speed. The values are divided to 67 (max)\n",
    "- casual: count of casual users\n",
    "- registered: count of registered users\n",
    "- cnt: count of total rental bikes including both casual and registered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sachin/anaconda/lib/python3.5/site-packages/pandas/core/ops.py:533: PerformanceWarning: Adding/subtracting array of DateOffsets to Series not vectorized\n",
      "  \"Series not vectorized\", PerformanceWarning)\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "      <th>time</th>\n",
       "      <th>t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>1.293840e+18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>1.293844e+18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32</td>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>1.293847e+18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>1.293851e+18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>1.293854e+18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season  mnth  hr  holiday  weekday  workingday  weathersit  temp   hum  \\\n",
       "0       1     1   0        0        6           0           1  0.24  0.81   \n",
       "1       1     1   1        0        6           0           1  0.22  0.80   \n",
       "2       1     1   2        0        6           0           1  0.22  0.80   \n",
       "3       1     1   3        0        6           0           1  0.24  0.75   \n",
       "4       1     1   4        0        6           0           1  0.24  0.75   \n",
       "\n",
       "   windspeed  cnt                time             t  \n",
       "0        0.0   16 2011-01-01 00:00:00  1.293840e+18  \n",
       "1        0.0   40 2011-01-01 01:00:00  1.293844e+18  \n",
       "2        0.0   32 2011-01-01 02:00:00  1.293847e+18  \n",
       "3        0.0   13 2011-01-01 03:00:00  1.293851e+18  \n",
       "4        0.0    1 2011-01-01 04:00:00  1.293854e+18  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rides = pd.read_csv('hour.csv')\n",
    "rides['time'] = pd.to_datetime(rides['dteday'])+pd.Series([pd.DateOffset(hours = a ) for a in rides.hr.tolist()])\n",
    "rides.drop(['instant','yr','dteday','casual','registered', 'atemp'],1,inplace=True)\n",
    "rides['t'] = rides.time.values.astype(float)\n",
    "rides.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get **one hot encoded** values of the categorical variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "   temp   hum  windspeed  cnt                time             t  season_1  \\\n",
       "0  0.24  0.81        0.0   16 2011-01-01 00:00:00  1.293840e+18         1   \n",
       "1  0.22  0.80        0.0   40 2011-01-01 01:00:00  1.293844e+18         1   \n",
       "2  0.22  0.80        0.0   32 2011-01-01 02:00:00  1.293847e+18         1   \n",
       "3  0.24  0.75        0.0   13 2011-01-01 03:00:00  1.293851e+18         1   \n",
       "4  0.24  0.75        0.0    1 2011-01-01 04:00:00  1.293854e+18         1   \n",
       "\n",
       "   season_2  season_3  season_4      ...       weekday_3  weekday_4  \\\n",
       "0         0         0         0      ...               0          0   \n",
       "1         0         0         0      ...               0          0   \n",
       "2         0         0         0      ...               0          0   \n",
       "3         0         0         0      ...               0          0   \n",
       "4         0         0         0      ...               0          0   \n",
       "\n",
       "   weekday_5  weekday_6  workingday_0  workingday_1  weathersit_1  \\\n",
       "0          0          1             1             0             1   \n",
       "1          0          1             1             0             1   \n",
       "2          0          1             1             0             1   \n",
       "3          0          1             1             0             1   \n",
       "4          0          1             1             0             1   \n",
       "\n",
       "   weathersit_2  weathersit_3  weathersit_4  \n",
       "0             0             0             0  \n",
       "1             0             0             0  \n",
       "2             0             0             0  \n",
       "3             0             0             0  \n",
       "4             0             0             0  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_features = ['season', 'hr', 'mnth', 'holiday', 'weekday', 'workingday', 'weathersit']\n",
    "rides = pd.get_dummies(rides, columns=cat_features)\n",
    "rides.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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PEkKOEUIeIYTc0MsnnwVmFmsAgH3To1istVb13E5eP5fR7JmtL81xNKdr0Ei8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\nnAgDY6yRzD7j4/7EUAFLtZa0rCLD7Ic4Zv+0OxL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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113b283c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(rides.time[:240],rides.cnt[:240])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sachin/anaconda/lib/python3.5/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "      <th>time</th>\n",
       "      <th>t</th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "      <th>...</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "      <th>workingday_0</th>\n",
       "      <th>workingday_1</th>\n",
       "      <th>weathersit_1</th>\n",
       "      <th>weathersit_2</th>\n",
       "      <th>weathersit_3</th>\n",
       "      <th>weathersit_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.334648</td>\n",
       "      <td>0.947372</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>-0.956339</td>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>-1.745435</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.438516</td>\n",
       "      <td>0.895539</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>-0.824022</td>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>-1.745236</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.438516</td>\n",
       "      <td>0.895539</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>-0.868128</td>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>-1.745038</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.334648</td>\n",
       "      <td>0.636370</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>-0.972879</td>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>-1.744840</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.334648</td>\n",
       "      <td>0.636370</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>-1.039037</td>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>-1.744641</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       temp       hum  windspeed       cnt                time         t  \\\n",
       "0 -1.334648  0.947372  -1.553889 -0.956339 2011-01-01 00:00:00 -1.745435   \n",
       "1 -1.438516  0.895539  -1.553889 -0.824022 2011-01-01 01:00:00 -1.745236   \n",
       "2 -1.438516  0.895539  -1.553889 -0.868128 2011-01-01 02:00:00 -1.745038   \n",
       "3 -1.334648  0.636370  -1.553889 -0.972879 2011-01-01 03:00:00 -1.744840   \n",
       "4 -1.334648  0.636370  -1.553889 -1.039037 2011-01-01 04:00:00 -1.744641   \n",
       "\n",
       "   season_1  season_2  season_3  season_4      ...       weekday_3  weekday_4  \\\n",
       "0         1         0         0         0      ...               0          0   \n",
       "1         1         0         0         0      ...               0          0   \n",
       "2         1         0         0         0      ...               0          0   \n",
       "3         1         0         0         0      ...               0          0   \n",
       "4         1         0         0         0      ...               0          0   \n",
       "\n",
       "   weekday_5  weekday_6  workingday_0  workingday_1  weathersit_1  \\\n",
       "0          0          1             1             0             1   \n",
       "1          0          1             1             0             1   \n",
       "2          0          1             1             0             1   \n",
       "3          0          1             1             0             1   \n",
       "4          0          1             1             0             1   \n",
       "\n",
       "   weathersit_2  weathersit_3  weathersit_4  \n",
       "0             0             0             0  \n",
       "1             0             0             0  \n",
       "2             0             0             0  \n",
       "3             0             0             0  \n",
       "4             0             0             0  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quant_features = ['temp', 'hum', 'windspeed','t']\n",
    "x_scaler = StandardScaler()\n",
    "rides[quant_features] = x_scaler.fit_transform(rides[quant_features].values)\n",
    "\n",
    "y_scaler = StandardScaler()\n",
    "rides['cnt'] = y_scaler.fit_transform(rides['cnt'].values[:,None])\n",
    "\n",
    "rides.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial model (1 hidden layer with Relu activations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split data into testing and training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data = rides[:-21*24]\n",
    "test_data = rides[-21*24:]\n",
    "\n",
    "train_x = train_data.drop(['cnt','time'],axis=1).values\n",
    "test_x = test_data.drop(['cnt','time'],axis=1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16875, 59)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at https://keras.io/#getting-started-30-seconds-to-keras for the architecture.\n",
    "Hints: \n",
    "1. Let `input_dim = train_x.shape[1]`.\n",
    "2. Let the last layer be `Dense(1)` with no activation\n",
    "3. For the `model.compile` part let `loss='mse'`(mean squared error). As for the optimizer part, choose one from the following: https://imgur.com/a/Hqolp (there is no NAG or Momentum optimizers though)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "# TODO get a one hidden layer network (let the number of hidden units be half way between input and last layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/11\n",
      "16875/16875 [==============================] - 0s - loss: 1.0494     \n",
      "Epoch 2/11\n",
      "16875/16875 [==============================] - 0s - loss: 0.7263     \n",
      "Epoch 3/11\n",
      "16875/16875 [==============================] - 0s - loss: 0.5620     \n",
      "Epoch 4/11\n",
      "16875/16875 [==============================] - 0s - loss: 0.4589     \n",
      "Epoch 5/11\n",
      "16875/16875 [==============================] - 0s - loss: 0.3864     \n",
      "Epoch 6/11\n",
      "16875/16875 [==============================] - 0s - loss: 0.3356     \n",
      "Epoch 7/11\n",
      "16875/16875 [==============================] - 0s - loss: 0.3007     \n",
      "Epoch 8/11\n",
      "16875/16875 [==============================] - 0s - loss: 0.2766     \n",
      "Epoch 9/11\n",
      "16875/16875 [==============================] - 0s - loss: 0.2584     \n",
      "Epoch 10/11\n",
      "16875/16875 [==============================] - 0s - loss: 0.2431     \n",
      "Epoch 11/11\n",
      "16875/16875 [==============================] - 0s - loss: 0.2293     \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x119033ac8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model\n",
    "model.fit(train_x, train_data['cnt'], batch_size=256, epochs=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 29)                1740      \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 29)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 30        \n",
      "=================================================================\n",
      "Total params: 1,770.0\n",
      "Trainable params: 1,770.0\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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B4LUAztX/3gng4w29h4mSPTi3daAWkyzQWqNFYebEgkT02mUyMxbqW1otCIIgiIaoLaIZ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z0LRHtN9mIOqoBoI5dzBJyJ7pmrMAjQ0rCcM7w2WhijP9YD0RYSeUwA2DNWMCY+ttGe6v4xLcRi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wsn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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119051c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_y = # TODO: predict on the test set (again refer to 30 seconds with keras link above)\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.plot(test_data.time, y_scaler.inverse_transform(test_data.cnt), label='Actual')\n",
    "plt.plot(test_data.time, y_scaler.inverse_transform(pred_y), label='Prediction')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's important that you understand the difference between `model.predict` and `model.evaluate`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "256/504 [==============>...............] - ETA: 0s"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.39349144981020973"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(test_x, test_data['cnt'], batch_size=256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#! pip install graphviz\n",
    "#! pip install pydot-ng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 260)\">\n",
       "<title>G</title>\n",
       "<polygon fill=\"white\" points=\"-4,4 -4,-260 172,-260 172,4 -4,4\" stroke=\"none\"/>\n",
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       "<polygon fill=\"none\" points=\"0,-219.5 0,-255.5 168,-255.5 168,-219.5 0,-219.5\" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"84\" y=\"-233.8\">dense_1_input: InputLayer</text>\n",
       "</g>\n",
       "<!-- 4625398080 -->\n",
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       "<polygon fill=\"none\" points=\"31.5,-146.5 31.5,-182.5 136.5,-182.5 136.5,-146.5 31.5,-146.5\" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"84\" y=\"-160.8\">dense_1: Dense</text>\n",
       "</g>\n",
       "<!-- 4638338632&#45;&gt;4625398080 -->\n",
       "<g class=\"edge\" id=\"edge1\"><title>4638338632-&gt;4625398080</title>\n",
       "<path d=\"M84,-219.313C84,-211.289 84,-201.547 84,-192.569\" fill=\"none\" stroke=\"black\"/>\n",
       "<polygon fill=\"black\" points=\"87.5001,-192.529 84,-182.529 80.5001,-192.529 87.5001,-192.529\" stroke=\"black\"/>\n",
       "</g>\n",
       "<!-- 4625395840 -->\n",
       "<g class=\"node\" id=\"node3\"><title>4625395840</title>\n",
       "<polygon fill=\"none\" points=\"8,-73.5 8,-109.5 160,-109.5 160,-73.5 8,-73.5\" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"84\" y=\"-87.8\">activation_1: Activation</text>\n",
       "</g>\n",
       "<!-- 4625398080&#45;&gt;4625395840 -->\n",
       "<g class=\"edge\" id=\"edge2\"><title>4625398080-&gt;4625395840</title>\n",
       "<path d=\"M84,-146.313C84,-138.289 84,-128.547 84,-119.569\" fill=\"none\" stroke=\"black\"/>\n",
       "<polygon fill=\"black\" points=\"87.5001,-119.529 84,-109.529 80.5001,-119.529 87.5001,-119.529\" stroke=\"black\"/>\n",
       "</g>\n",
       "<!-- 4711109016 -->\n",
       "<g class=\"node\" id=\"node4\"><title>4711109016</title>\n",
       "<polygon fill=\"none\" points=\"31.5,-0.5 31.5,-36.5 136.5,-36.5 136.5,-0.5 31.5,-0.5\" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"84\" y=\"-14.8\">dense_2: Dense</text>\n",
       "</g>\n",
       "<!-- 4625395840&#45;&gt;4711109016 -->\n",
       "<g class=\"edge\" id=\"edge3\"><title>4625395840-&gt;4711109016</title>\n",
       "<path d=\"M84,-73.3129C84,-65.2895 84,-55.5475 84,-46.5691\" fill=\"none\" stroke=\"black\"/>\n",
       "<polygon fill=\"black\" points=\"87.5001,-46.5288 84,-36.5288 80.5001,-46.5289 87.5001,-46.5288\" stroke=\"black\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "\n",
    "SVG(model_to_dot(model).create(prog='dot', format='svg'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Two Layer hidden network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16875, 59)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/11\n",
      "16875/16875 [==============================] - 0s - loss: 0.7995     \n",
      "Epoch 2/11\n",
      "16875/16875 [==============================] - 0s - loss: 0.5246     \n",
      "Epoch 3/11\n",
      "16875/16875 [==============================] - 0s - loss: 0.3446     \n",
      "Epoch 4/11\n",
      "16875/16875 [==============================] - 0s - loss: 0.2460     \n",
      "Epoch 5/11\n",
      "16875/16875 [==============================] - 0s - loss: 0.1982     \n",
      "Epoch 6/11\n",
      "16875/16875 [==============================] - 0s - loss: 0.1675     \n",
      "Epoch 7/11\n",
      "16875/16875 [==============================] - 0s - loss: 0.1462     \n",
      "Epoch 8/11\n",
      "16875/16875 [==============================] - 0s - loss: 0.1296     \n",
      "Epoch 9/11\n",
      "16875/16875 [==============================] - 0s - loss: 0.1161     \n",
      "Epoch 10/11\n",
      "16875/16875 [==============================] - 0s - loss: 0.1045     \n",
      "Epoch 11/11\n",
      "16875/16875 [==============================] - 0s - loss: 0.0950     \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x119861f28>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h_nodes = [20, 40]\n",
    "# TODO: two layer hidden network with the number of hidden units as mentioned in `h_nodes`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "256/504 [==============>...............] - ETA: 0s"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.28692415971604607"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(test_x, test_data['cnt'], batch_size=256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_3 (Dense)              (None, 40)                2400      \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 40)                0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 20)                820       \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 20)                0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 1)                 21        \n",
      "=================================================================\n",
      "Total params: 3,241.0\n",
      "Trainable params: 3,241.0\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<svg height=\"410pt\" viewBox=\"0.00 0.00 176.00 410.00\" width=\"176pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 406)\">\n",
       "<title>G</title>\n",
       "<polygon fill=\"white\" points=\"-4,4 -4,-406 172,-406 172,4 -4,4\" stroke=\"none\"/>\n",
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       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"84\" y=\"-379.8\">dense_3_input: InputLayer</text>\n",
       "</g>\n",
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       "<polygon fill=\"none\" points=\"31.5,-292.5 31.5,-328.5 136.5,-328.5 136.5,-292.5 31.5,-292.5\" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"84\" y=\"-306.8\">dense_3: Dense</text>\n",
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       "<g class=\"edge\" id=\"edge1\"><title>4717623840-&gt;4717623392</title>\n",
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       "<polygon fill=\"none\" points=\"8,-219.5 8,-255.5 160,-255.5 160,-219.5 8,-219.5\" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"84\" y=\"-233.8\">activation_2: Activation</text>\n",
       "</g>\n",
       "<!-- 4717623392&#45;&gt;4717623336 -->\n",
       "<g class=\"edge\" id=\"edge2\"><title>4717623392-&gt;4717623336</title>\n",
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       "<g class=\"node\" id=\"node4\"><title>4717649312</title>\n",
       "<polygon fill=\"none\" points=\"31.5,-146.5 31.5,-182.5 136.5,-182.5 136.5,-146.5 31.5,-146.5\" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"84\" y=\"-160.8\">dense_4: Dense</text>\n",
       "</g>\n",
       "<!-- 4717623336&#45;&gt;4717649312 -->\n",
       "<g class=\"edge\" id=\"edge3\"><title>4717623336-&gt;4717649312</title>\n",
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       "<polygon fill=\"black\" points=\"87.5001,-192.529 84,-182.529 80.5001,-192.529 87.5001,-192.529\" stroke=\"black\"/>\n",
       "</g>\n",
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       "<g class=\"node\" id=\"node5\"><title>4717646904</title>\n",
       "<polygon fill=\"none\" points=\"8,-73.5 8,-109.5 160,-109.5 160,-73.5 8,-73.5\" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"84\" y=\"-87.8\">activation_3: Activation</text>\n",
       "</g>\n",
       "<!-- 4717649312&#45;&gt;4717646904 -->\n",
       "<g class=\"edge\" id=\"edge4\"><title>4717649312-&gt;4717646904</title>\n",
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       "<polygon fill=\"none\" points=\"31.5,-0.5 31.5,-36.5 136.5,-36.5 136.5,-0.5 31.5,-0.5\" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"84\" y=\"-14.8\">dense_5: Dense</text>\n",
       "</g>\n",
       "<!-- 4717646904&#45;&gt;4718427608 -->\n",
       "<g class=\"edge\" id=\"edge5\"><title>4717646904-&gt;4718427608</title>\n",
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       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVG(model_to_dot(model).create(prog='dot', format='svg'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Best Hyper parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is Cross Validation?\n",
    "https://stats.stackexchange.com/questions/1826/cross-validation-in-plain-english"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import keras.backend as K\n",
    "\n",
    "def create_model(layers, activation):\n",
    "    model = Sequential()\n",
    "    for i, nodes in enumerate(layers):\n",
    "        if i==0:\n",
    "            # TODO: Add a dense layer\n",
    "            model.add(Activation(activation))\n",
    "        else:\n",
    "            # TODO: Add a Dense later AND activation (see above)\n",
    "            \n",
    "    #TODO: Add last dense layer # Note: no activation beyond this point\n",
    "    \n",
    "    model.compile(optimizer='adadelta', loss='mse')\n",
    "    return model\n",
    "    \n",
    "model = KerasRegressor(build_fn=create_model, verbose=0)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "layers = [[30], [20, 40], [15, 30, 40]]\n",
    "activations = [sigmoid, relu]\n",
    "param_grid = dict(layers=layers, activation=activations, batch_size = [256], epochs=[30])\n",
    "grid = GridSearchCV(estimator=model, param_grid=param_grid, scoring='neg_mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "grid_result = grid.fit(train_x, train_data['cnt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-0.15730031157267707,\n",
       " {'activation': <function keras.activations.relu>,\n",
       "  'batch_size': 256,\n",
       "  'epochs': 30,\n",
       "  'layers': [15, 30, 40]}]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[grid_result.best_score_, grid_result.best_params_]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.368126 (0.164836) with: {'batch_size': 256, 'layers': [30], 'epochs': 30, 'activation': <function sigmoid at 0x1130b9400>}\n",
      "-0.343356 (0.157840) with: {'batch_size': 256, 'layers': [20, 40], 'epochs': 30, 'activation': <function sigmoid at 0x1130b9400>}\n",
      "-0.322100 (0.167121) with: {'batch_size': 256, 'layers': [15, 30, 40], 'epochs': 30, 'activation': <function sigmoid at 0x1130b9400>}\n",
      "-0.206197 (0.104541) with: {'batch_size': 256, 'layers': [30], 'epochs': 30, 'activation': <function relu at 0x1130b92f0>}\n",
      "-0.164863 (0.084932) with: {'batch_size': 256, 'layers': [20, 40], 'epochs': 30, 'activation': <function relu at 0x1130b92f0>}\n",
      "-0.157300 (0.067843) with: {'batch_size': 256, 'layers': [15, 30, 40], 'epochs': 30, 'activation': <function relu at 0x1130b92f0>}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sachin/anaconda/lib/python3.5/site-packages/sklearn/model_selection/_search.py:662: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "for params, mean_score, scores in grid_result.grid_scores_:\n",
    "    print(\"%f (%f) with: %r\" % (scores.mean(), scores.std(), params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'activation': <function keras.activations.relu>,\n",
       " 'batch_size': 256,\n",
       " 'epochs': 30,\n",
       " 'layers': [15, 30, 40]}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_result.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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